Biology subjects

Theis, F. J.

Publications and source records attributed to Theis, F. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

IDTrack: Time- and Namespace-Aware Identifier Harmonization for Reproducible Workflows

MotivationReproducibility in computational biology fails silently when gene identifiers drift beneath unchanged analysis code: the same frozen pipeline, rerun months later, yields different results not because biology evolved but because identifier semantics shifted with upstream annotation releases--a failure mode invisible to version control and containerization because the mapping layer itself constitutes an undeclared coordinate system whose time axis advances independently of downstream workflows. Gene identifiers occupy positions in a joint space of namespace, annotation release, genome assembly, and entity layer that evolves through retirements, merges, splits, and nomenclature reassignments, so atlas integration, retrospective reanalysis, and perturbation screens inherit temporal dependencies that existing utilities cannot surface: current mappers answer what an identifier resolves to now rather than under what declared contract the feature space was constructed. ResultsIDTrack reconceptualizes identifier harmonization as a time-indexed coordinate transformation by materializing annotation release history into a snapshot-bounded identifier graph and solving conversions through a time-traveling, contract-constrained pathfinder that pins release boundaries, assembly contexts, and ambiguity policies as explicit parameters rather than implicit endpoint state. This architecture surfaces reachability and ambiguity as interpretable outcome classes--unmapped, uniquely resolved, or ambiguously multi-target--enables atlas-scale harmonization with explicit collision handling, and records every mapping decision in a provenance ledger that transforms invisible preprocessing into citable methodological infrastructure whose coordinate choices can be inspected, compared, and reproduced rather than lost as ephemeral preprocessing. Availability and ImplementationCode: https://github.com/theislab/idtrack; package: pip install idtrack. Contactkemal.inecik@helmholtz-munich.de; erkmenerken22@ku.edu.tr; fabian.theis@helmholtz-munich.de. Supplementary InformationSupplementary material elaborates on architectural decisions and implementation details.

bioinformatics↗

bioRxiv2026

Brain Region-Specific Oligodendrocyte States Highlight Mitochondrial Gene Upregulation and Loss of Canonical Identity Signatures in Alzheimers Disease

Oligodendrocyte dysfunction and heterogeneity are emerging as key contributors to Alzheimers disease (AD) pathology, though cellular mechanisms remain unclear. Using single-nucleus RNA sequencing across three cortical regions from the same individuals, we identified region- and disease-specific transcriptomic changes, including increased mitochondrial genes and loss of cell type signatures. These results highlight the importance of biologically-informed quality control and underscore the value of multi-regional analysis for understanding AD pathogenesis and progression.

bioinformatics↗

bioRxiv2025

DISCO-seq: 3D single-cell transcriptomics of intact biological systems

Single-cell transcriptomics has transformed tissue analysis, yet current methods struggle to integrate whole-tissue 3D architecture. Conventional techniques restrict molecular profiling to pre-selected 2D sections, losing systemic context and introducing anatomical bias by sampling less than 0.001% of a whole organism. To overcome these challenges, we developed DISCO-seq, a tissue-clearing chemistry that enables superior RNA accessibility compared to fresh or fixed tissues. DISCO-seq integrates whole-organ or organism 3D imaging with both untargeted and targeted transcriptomics, yielding high-quality RNA from cleared tissues comparable to standard samples. We demonstrate its versatility by investigating tumor heterogeneity in a syngeneic glioblastoma mouse model, using 3D imaging to identify spatially distinct microenvironments and characterize their unique transcriptomic signatures. Moreover, DISCO-seq enabled unbiased, whole-body mapping of SARS-CoV-2 S1 protein deposition in mice, followed by transcriptomic profiling of spatially defined niches. By bridgingmesoscale 3D imaging with single-cell transcriptomics, DISCO-seq establishes a paradigm for anatomically contextualized, hypothesis-free tissue interrogation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/693352v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@758ee5org.highwire.dtl.DTLVardef@1f862c6org.highwire.dtl.DTLVardef@1cf3b9org.highwire.dtl.DTLVardef@c5082e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISCO-seq integrates RNA-preserving tissue-clearing chemistry with whole-organ or organism 3D imaging, enabling anatomically unbiased single-cell transcriptomics. C_LIO_LIDISCO-seq yields RNA quality and transcriptome profiles equivalent to those obtained from matched fresh or fixed tissues. C_LIO_LIDISCO-seq identifies discrete glioblastoma microenvironments and defines their transcriptomic states within the intact brain. C_LIO_LIDISCO-seq enables whole-body mapping of SARS-CoV-2 S1 and uncovers region-specific immune and metabolic responses across anatomical niches. C_LI Supplementary movies can be seen at: http://discotechnologies.org/DISCO-seq/

systems biology↗

bioRxiv2025

Exploration of RNA outside segmented cells in spatial transcriptomics reveals extrasomatic RNA organization

Image-based spatial transcriptomics (iST) enables visualization of RNA molecules in their spatial context, yet up to 40% of transcripts remains unassigned to cells and has been largely overlooked. In this study, we systematically analyze unassigned RNAs (uRNAs) across 14 public iST datasets and multiple technologies to characterize their nature and relevance in tissue biology. By assessing potential technical origins, in particular segmentation errors, noise, and diffusion across many tissues in both humans and mice, we find that around one third of uRNAs cannot be attributed to technical artifacts. Those non-technical uRNAs are enriched around cells with complex morphologies such as neurons, glia, and endothelial cells and reflect transcripts localized in cellular protrusions and extrasomatic compartments. Using these signals, we infer protrusion-associated transcript localization and identify cell-cell contacts beyond standard cell-centric segmentation. Our results challenge the assumption that uRNA is purely technical noise and instead highlight its potential biological relevance, particularly in relation to intracellular RNA localization and tissue architecture. To enable their systematic study, we introduce troutpy, a Python package for quantitative uRNA exploration in spatial transcriptomics data.

bioinformatics↗

bioRxiv2025

Pseudodynamics+: Reconstructing Population Dynamics from Time-Resolved Single Cell Landscapes with Physics Informed Neural Networks

Single-cell profiling provides snapshots of the heterogeneous states that characterise developmental processes, organ regeneration and progression towards disease in a complex landscape. The underlying trajectories are of pivotal interest, but existing methods for reconstructing cell state trajectories commonly neglect population sizes. However, snapshot experiments make it difficult to interpret cell flux because the observed trajectories are confounded by changes in overall population size. This ambiguity can lead to misinterpreting changes in proliferation or death rates as changes in cellular migration. We introduce pseudodynamics+, a physics-informed neural network framework that solves high-dimensional flow equations on complex, branching landscapes. By integrating single-cell genomics with population dynamics, pseudodynamics+ estimates state- and time-dependent parameters of growth, differentiation, and diffusion. The model recapitulates proliferation bursts during T-cell maturation and, when applied to LARRY-barcoded data, predicts differentiation rates consistent with clonal behaviour. When applied to time-resolved persistent-labelling datasets of in vivo mouse bone marrow haematopoiesis, pseudodynamics+ reconstructs continuous tissue flows with dynamic parameters aligned with known molecular signatures. Notably, simulations revealed a previously unrecognised shift from megakaryocyte-biased to balanced progenitor output, explained by evolving fate preferences of progenitor states, as revealed by simulations leveraging our estimated dynamic parameters. Pseudodynamics+ therefore establishes a population-aware framework for reconstructing single-cell population dynamics and is available at https://github.com/Gottgens-lab/pseudodynamics_plus.

bioinformatics↗

bioRxiv2025

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

Decoding cellular systems requires integrating diverse omics data, yet most models are trained from scratch on a single modality, restricting generalization. Here we present CellHermes, a biological language model that leverages pretrained large language models (LLMs) to integrate multimodal forms of omics data, such as transcriptomic profile and PPI network, through natural language for better omics understanding. By reformulating these datasets into question-answer pairs, CellHermes emulates multiple self-supervised learning paradigms within a single, universal, language-based framework with LORA fine-tuning on existing natural language-LLM, achieving comparable or even better performance compared to current single-cell foundation models. Within this framework, CellHermes functions as an encoder, a predictor and an explainer for supporting a range of downstream tasks. For encoder, CellHermes encodes biological entities with embeddings that enable accurate network discovery and cross-dataset generalization; For predictor, CellHermes unifies heterogeneous downstream tasks by translating them into natural language question-answers pairs, allowing a single model to perform multi-task prediction by instruction fine-tuning; For explainer, CellHermes explains molecular mechanisms by combining attention analysis with text-based reasoning, leveraging the interpretability and reasoning capabilities of LLMs. We evaluated the ability of CellHermes as an encoder on representing genes and cells using 5 gene-level downstream tasks and 5 diverse single-cell datasets across different tissues compared with other single cell foundation models. We also evaluated CellHermes as a predictor on BioUniBench, a new benchmark of 10 tasks across 7 databases for LLMs, including perturbation response, cell fitness estimation, gene-disease association, and cell type identification. All benchmarks suggest CellHermes can unify multiple biological tasks within one model without compromising performance. By applying CellHermes as an explainer for a melanoma patients dataset, it can uncover potential key genes on tumor-reactive T cells. Together, CellHermes establishes natural language as a unifying medium for omics, offering a foundation that, in the future, potentially enables a more integrated and interpretable research loop of biological representation, prediction, and interpretability.

bioinformatics↗

bioRxiv2025

FADVI: disentangled representation learning for robust integration of single-cell and spatial omics data

Integrating single-cell and spatial omics data remains challenging due to strong batch effects across experiments and platforms. Existing methods focus on minimizing these effects but cannot disentangle technical variation from true biological signals. Here, we present FADVI, a variational autoencoder framework partitioning the latent space into batch-specific, label-related, and residual subspaces. By combining supervised classification, adversarial training, and cross-covariance penalty, FADVI disentangles batch from biological representations, preserving biological variation while correcting batch effects. Benchmarking across scRNA-seq, scATAC-seq, and high-resolution spatial transcriptomics datasets, FADVI consistently outperforms state-of-the-art integration methods. FADVI also enables feature attribution for revealing genes associated with cell type identity and batch variation. Together, these results demonstrate that FADVI provides robust, interpretable integration for large-scale single-cell and spatial omics data, offering a powerful framework for downstream analysis and discovery.

bioinformatics↗

bioRxiv2025

scConcept: Contrastive pretraining for technology-agnostic single-cell representations beyond reconstruction

Recent large-scale single-cell foundation models have shown promise for exploring cellular states, yet they often underperform compared to simpler, domain-specific methods, raising concerns about their broader applicability. A key limitation lies in their reliance on masked language modeling, which is well suited for generative language tasks but poorly aligned with learning rich cell-level embeddings required in single-cell biology. Moreover, the proliferation of transcriptomic technologies--from whole transcriptome dissociated assays to image-based targeted profiling--poses a major challenge for cross-platform generalization. Here, we align with recent advances in machine learning to move beyond reconstruction metrics, which often do not capture important sample variation. We present scConcept ("contrastive cell pre-training"), a transformer-based contrastive learning framework that directly optimizes cell embeddings by contrasting multiple views of cells. By replacing gene-level reconstruction with a cell-level identification task, scConcept learns robust representations that are invariant to count distributions and gene panel selection, across diverse assays and technologies. To highlight the capability of the proposed framework, we pretrain scConcept on a similar corpus of over 30 million single-cell RNA-seq profiles as recent foundation models. Our approach demonstrates superior performance not only compared to state-of-the-art pretrained foundation models but also domain-specific methods in various downstream tasks, including cell-type annotation, technology integration, dissociated to spatial cell-type transfer, spatial imputation, gene panel optimization, and mapping new technologies on already existing atlases. Our results highlight contrastive pretraining as a powerful alternative to reconstruction-based strategies for single-cell modeling, providing a path toward general-purpose, technology-agnostic cell representations.

bioinformatics↗

bioRxiv2025

scPortrait integrates single-cell images into multimodal modeling

Machine learning increasingly uncovers rules of biology directly from data, enabled by large, standardized datasets. Microscopy images provide rich information on cellular architecture and are accessible at scale across biological systems, making them an ideal foundation for modeling cell behavior. However, a standardized image format does not exist at the single-cell level. Here we present scPortrait, an scverse software package for generation, storage, and application of single-cell image datasets. scPortrait reads, stitches and segments raw fields of view with out-of-core computation scaling to larger-than-memory datasets. Parallelization enables rapid extraction of individual cells into a standardized single-cell image format with fast access to accelerate machine learning. scPortrait enables analysis across modalities including images, proteomics and transcriptomics, identifying cancer-associated macrophage subpopulations by morphology and embedding single-cell images into transcriptome atlases. scPortrait turns microscopy images into a reusable resource for integrative cell modeling, establishing single-cell images as a core modality in systems biology.

systems biology↗

bioRxiv2025

OmniPath: integrated knowledgebase for multi-omics analysis

Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, miRNA, and enzyme-PTM (post-translational modification) interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model (LLM) agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell and spatial multi-omics data, especially for mechanistic and causal modeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/675512v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17c2b6borg.highwire.dtl.DTLVardef@1069835org.highwire.dtl.DTLVardef@1f2ce76org.highwire.dtl.DTLVardef@1d0b34f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Single cell decomposition of multicellular aging programs associated with impaired lung regeneration

Aging impairs the regenerative capacity of mammalian organs and is a major risk factor for organ fibrosis. Mechanisms underlying persistent fibrosis after lung injury in old individuals remain unclear. We used longitudinal single-cell RNA-seq after lung injury and dissected aging effects computationally and experimentally at baseline and during repair. In old mice, sustained fibroblast activation in the resolution phase of fibrosis was associated with prolonged epithelial senescence and persistent epithelial-mesenchymal crosstalk. Single-cell interpretable tensor decomposition analysis revealed that aging most strongly affected T/B-lymphocytes and macrophages. Notably, we identified a Granzyme K-high CD8+ T cell state that was unique to aged mice, co-localized with epithelial progenitors, and its co-culture or Gzmk treatments in lung organoids impaired progenitor function by inducing stem cell senescence. In summary, our study highlights the effects of immune aging on epithelial progenitor function and provides a time-resolved high resolution map of lung regeneration in the context of aging.

cell biology↗

bioRxiv2025

Beyond Visual Inspection: Principled Benchmarking of Single-Cell Trajectory Representations with scTRAM

Single-cell representation learning aims to construct low-dimensional embeddings that preserve biologically salient manifold structures. Yet current evaluation protocols remain inadequate for assessing trajectory fidelity--essential for studying cell differentiation, immune responses, disease progression and perturbation--relying on heuristic visualizations or scalar surrogates that overlook multiscale trajectory geometry. To address this gap, we present scTRAM--single-cell trajectory representation metrics--a principled benchmarking framework that quantitatively evaluates how well single-cell embeddings retain groundtruth trajectories across complementary failure modes, from local neighborhood scrambling to global branch mis-ordering. Through our experiments, we show that scTRAM reveals modelspecific trade-offs in preserving distinct aspects of trajectory structure, with edge-specific decomposition exposing localized representational biases at high resolution. Systematic validation across diverse biological contexts confirms that the metric suite is sensitive to genuine structural properties rather than statistical artifacts. By shifting from qualitative, ad-hoc judgments to quantitative, principled approach, scTRAM enables rigorous comparison of embedding methods and informs downstream analyses where trajectory integrity is essential for biological interpretation.

bioinformatics↗

bioRxiv2025

Biologically Guided Variational Inference for Interpretable Multimodal Single-Cell Integration and Mechanistic Discovery

Multi-omics technologies allow detailed characterization of cell types and states across omics layers as well as chemical and genetic perturbations. Variational autoencoders have become a cornerstone of single-cell data integration; however, they are often implemented as black-box models, requiring post hoc interpretation using known markers, pathways, or regulators to make sense of their latent representations. NetworkVI fundamentally flips this paradigm by incorporating biological knowledge directly into the model architecture. By embedding co-regulation networks derived from topologically associated domains and structured ontologies such as the Gene Ontology (GO), NetworkVI introduces a biologically-informed inductive bias that promotes the preservation of meaningful variation during integration while enforcing interpretability at both the gene and GO levels. NetworkVI achieves state-of-the-art data integration, modality imputation, and cell label transfer across bimodal and trimodal datasets. Beyond integration, here we show that NetworkVI facilitates ontology-guided hypothesis generation by exploiting established associations between genes, structured cellular programs, and regulatory domains to interpretably model cellular identities. Furthermore, decomposition of GO activation spaces resolves lineage-specific functional states within immune cell types, including quiescent, inflammatory, and transitional monocyte subpopulations, that are invisible to transcriptomic clustering. NetworkVI prioritizes GO-term programs associated with immunosenescence, consistent with age-associated immune dysregulation and reveals candidate immune evasion mechanisms consistent with CD58 loss in a Perturb-CITE-seq melanoma dataset.

bioinformatics↗

bioRxiv2025

Automated evaluation of single-cell reference atlas mappings enables the identification of disease-associated cell states

The rise of single-cell atlases has opened up the use of reference atlases as a comprehensive healthy control for the study of disease. A key step for using atlases is the mapping of "query" disease or perturbed datasets onto a healthy reference atlas, which allows for comparison of the query to healthy controls and thereby the identification of perturbation-specific transcriptional cell states. However, the mapping success, i.e. the extent to which these mappings correctly reflect biological similarities and differences between query and reference, varies with e.g. experimental confounders in the data (dissociation protocol, single-cell assay) or the mapping method used, and cannot be predicted in advance. Moreover, an unsuccessful mapping can result in falsely reporting technical artifacts as disease or perturbation-specific cellular changes and in overlooking true altered states. Here, we present MapQC, a method that quantifies query-to-reference mapping success by leveraging the information present in the reference embedding. Specifically, MapQC tests the presence of remaining batch effect in the embedding after mapping by comparing inter-sample distances in the healthy reference itself to distance of query control samples to the reference. Similarly, it tests whether disease or perturbation-specific variation has been retained during mapping by testing whether query perturbed samples are more distant to the reference than reference samples are from each other. We apply MapQC to lung and endometrial query datasets, including data of idiopathic pulmonary fibrosis and Asherman Syndrome, mapped to large-scale healthy references. We show that MapQC correctly distinguishes successful mappings from unsuccessful ones where data visualization techniques such as UMAP can be deceiving, and that mapQC outperforms integration metrics often re-purposed for mapping qualitycontrol. Moreover, only mappings that MapQC identified as successful result in the correct identification of cell state changes specific to disease. Taken together, MapQC provides a critical quality-control metric for a more reliable, robust, and accurate use of reference atlases to study disease.

bioinformatics↗

bioRxiv2025

CellFlow enables generative single-cell phenotype modeling with flow matching

High-content phenotypic screens provide a powerful strategy for studying biological systems, but the scale of possible perturbations and cell states makes exhaustive experiments unfeasible. Computational models that are trained on existing data and extrapolate to correctly predict outcomes in unseen contexts have the potential to accelerate biological discovery. Here, we present CellFlow, a flexible framework based on flow matching that can model single cell phenotypes induced by complex perturbations. We apply CellFlow to various phenotypic screens, accurately predicting expression responses to a wide range of perturbations, including cytokine stimulation, drug treatments and gene knockouts. CellFlow successfully modeled developmental perturbations at the whole-embryo scale and guided cell fate and organoid engineering by predicting heterogeneous cell populations arising from combinatorial morphogen treatments and by performing a virtual organoid protocol screen. Taken together, CellFlow has the potential to accelerate discovery from phenotypic screens by learning from existing data and generating phenotypes induced by unseen conditions.

bioinformatics↗

bioRxiv2025

TNF-α disrupts the malate-aspartate shuttle, driving metabolic rewiring in iPSC-derived enteric neural lineages from Parkinson's Disease patients

Gastrointestinal (GI) dysfunction emerges years before motor symptoms in Parkinsons disease (PD), implicating the enteric nervous system (ENS) in early disease progression. However, the mechanisms linking the PD hallmark protein, -synuclein (-syn), to ENS dysfunction - and whether these mechanisms are influenced by inflammation - remains elusive. Using iPSC-derived enteric neural lineages from patients with -syn triplications, we reveal that TNF- increases mitochondrial--syn interactions, disrupts the malate-aspartate shuttle, and forces a metabolic shift toward glutamine oxidation. These alterations drive mitochondrial dysfunction, characterizing metabolic impairment under cytokine stress. Interestingly, targeting glutamate metabolism with Chicago Sky Blue 6B restores mitochondrial function, reversing TNF--driven metabolic disruption. Our findings position the ENS as a central player in PD pathogenesis, establishing a direct link between cytokines, -syn accumulation, metabolic stress and mitochondrial dysfunction. By uncovering a previously unrecognized metabolic vulnerability in the ENS, we highlight its potential as a therapeutic target for early PD intervention.

neuroscience↗

bioRxiv2025

TGIF2 is a major regulator of neural stem cell fate and neurogenic priming

During brain development, neural stem cells (NSCs) must balance self-renewal with differentiation and ensure lineage progression. To identify novel regulators of NSCs during neurogenesis, we isolated NSCs by FACS from the mouse cerebral cortex and ganglionic eminence at mid-neurogenesis, and at birth, when gliogenesis starts in both, but neurogenesis only continues in the latter region. RNA-seq and ATAC-seq revealed major transcriptional and chromatin changes between these stages and identified TGFB-Induced Homeobox Factor 2 (TGIF2) as a key candidate factor in neurogenic NSCs. In vitro and in vivo experiments demonstrated a potent role of TGIF2 controlling NSC fate maintenance mediated by its interaction with the SIN3A/HDAC repressor complex suppressing neuronal differentiation genes. Multiomic comparison of NSC and neuron gene expression allowed the comprehensive analysis of neurogenic priming in cortical NSCs, identifying TGIF2 as its major regulator by restraining neuronal differentiation gene activation in NSCs.

neuroscience↗

bioRxiv2025

2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices

Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in 3D. Cell segmentation assigns transcripts to cells and precedes annotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial doublets arising from overlapping cells, resulting in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds, and inaccurate cell-segmentation by analyzing transcript localization in 3D.

bioinformatics↗